Skill UI

This guide assists researchers in determining the suitability of their study for The Lancet Public Health. It outlines the scope, emphasizing population-level interventions, global epidemiology, and health policy. It details stringent requirements for methodology, reporting guidelines (STROBE, CONSORT, PRISMA), addressing biases, and adhering to specialized journal house styles and submission protocols.

This skill provides comprehensive guidance for submitting high-impact clinical and population research to The Lancet Respiratory Medicine. It details the journal's specialized scope, which favors practice-changing randomized trials and major prospective cohorts in respiratory and critical care. The guide highlights the rigorous evidence bar, including expected reporting guidelines (CONSORT, STROBE, PRISMA), statistical rigor, and structural house style, serving as a crucial fit and framing aid for authors.

A comprehensive guide for designing, auditing, and reporting advanced statistical and causal inference experiments. It emphasizes moving beyond simple accuracy metrics to quantify uncertainty, model robustness, and causal mechanisms. Covers metrics like coverage, KL divergence, and regret, and outlines best practices for seeding, compute fairness, and creating standardized reporting blocks (e.g., UAI standards).

Provides comprehensive guidelines for designing rigorous technical evaluations for interactive systems, specifically tailored for top-tier venues like UIST. It emphasizes matching evaluation methods to specific artifact claims, reporting distributions over means, closing the interaction loop, and performing thorough ablations against real-world baselines. This framework ensures the scientific rigor and credibility of submitted research.

This skill enables rigorous tracking of physical units and the systematic propagation of measurement uncertainty in scientific calculations. It supports unit conversions, building comprehensive GUM uncertainty budgets, and performing dimensional and physical plausibility checks. Use it whenever a calculation requires adherence to strict metrological standards and accurate error reporting.

This guide provides best practices for optimizing the presentation of data exhibits—including tables, figures, and qualitative displays—in development economics manuscripts. It ensures that complex findings are highly legible and actionable for a multidisciplinary, policy-oriented audience, moving beyond simple statistical reporting to communicate clear, impactful narratives.

This skill provides comprehensive norms for executing and reporting robust, reproducible data analysis in comparative, cross-national, or formal-empirical studies. It guides authors on honestly reporting uncertainty, ensuring statistical robustness, performing rigorous cross-national inference, and integrating qualitative evidence through triangulation to meet the demanding standards of top academic journals.

This guide provides a comprehensive framework for designing rigorous and defensible evaluation sections for machine learning and information retrieval papers, particularly for top-tier conferences like WSDM. It covers the four critical evidence quadrants—Effectiveness, Validity, Efficiency, and Robustness—and details necessary protocols, such as temporal splits, proper baseline selection (including heuristics), ablation studies, and hardware-aware performance reporting. Use this when auditing or writing the experimental section of a research paper.

A comprehensive framework for ensuring reproducibility in Web Search and Data Mining (WSDM) research. It addresses unique challenges associated with behavioral data, covering data provenance, temporal splitting discipline, bias assumption reporting, statistical rigor (seeds, variance), and the 'honesty ladder' for non-reproducible results, helping researchers publish robust and verifiable work.